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Generative AI timeline Credit- David

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Various types of test used in statistics for data science T-test: used to test whether the means of two groups are significantly different from each other. ANOVA: used to test whether the means of three or more groups are significantly different from each other. Chi-squared test: used to test whether two categorical variables are independent or associated with each other. Pearson correlation test: used to test whether there is a significant linear relationship between two continuous variables. Wilcoxon signed-rank test: used to test whether the median of two related samples is significantly different from each other. Mann-Whitney U test: used to test whether the median of two independent samples is significantly different from each other. Kruskal-Wallis test: used to test whether the medians of three or more independent samples are significantly different from each other. Friedman test: used to test whether the medians of three or more related samples are significantly different from each other.

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What is online machine learning Online machine learning. Abhishek Singh Online machine learning Online machine learning is a type of machine learning that involves updating a model continuously based on new data points as they become available. In contrast to batch learning, where the model is trained on a fixed dataset, online learning adapts to new data incrementally and in real-time. Online learning is particularly useful in scenarios where data is constantly arriving and the model needs to be updated frequently to reflect the latest information. Examples include fraud detection, recommendation systems, and online advertising. In online learning, the model is initially trained on a small subset of the data, and as new data arrives, the model updates its parameters to incorporate the new information. The update process can be done using various algorithms, such as stochastic gradient descent or online gradient descent. Online learning has several advantages over batch learning, including the ability to adapt to changing data distributions, the ability to handle large datasets efficiently, and the ability to make real-time predictions. However, it also has some limitations, such as the need to carefully manage the learning rate to avoid overfitting, and the difficulty in handling non-stationary data streams.

How to data preprocessing speed using polar library. Polar is a powerful data preprocessing library which support parallel processing.

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